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Unlocking the Potential: A Comprehensive Guide to Collaborative Testing of the Pinterest App

Have you ever wondered how leading apps such as Pinterest ensure that they are robust, bug-free, and user-friendly? Collaborative testing is a dynamic process that harnesses the power of teamwork to elevate the quality of apps. An organization with diverse members collaborating to identify and optimize hidden glitches while keeping the user experience high. You’ll […]

Steam-achievements
Importance-in-app-development
Pinterest
Team-members
B-team-composition
Diverse-team-members
Collaborative-testing
Centric-evaluation
Centric-focus
Key-features
Feature-selection

Breakthrough Research Papers Solve Daunting Software Challenges and Shape the Future of the Industry

Natasha Mittal said This could revolutionize applications like smart cities and Industry 4.0, where real-time processing at the edge and complex computations in the cloud are essential

Mittal-coo
Natasha-mittal
International-conference-on-data-engineering
Trees-coo
Mcdonald
Professional-journey
Search-experience
Doordash-new
Generic-pollers
Uber-eats
Bridging-theory
Feature-selection

A Python Data Analysis Project to Understand Hotel Cancellations

Data Cleaning, Analysis, Visualization, Feature Selection, Predictive Modeling The hospitality industry flourishes by providing outstanding guest experiences;…

Data-cleaning
Feature-selection
News
Ggregator
Reaking-news
Uration
Media

Exploring Exciting New Features in Java 17

In this blog, we will learn about 5 new java features: 1. Sealed Classes 2. Pattern Matching for Switch 3. Foreign Function Interface (FFI) 4. Memory API 5. Text Block

Java-program
Function-interface
Java-native-interface
Java-virtual-machine
Text-blocks
Data
Lava
Memory
String
Data-type
Feature-selection

"Predicting lung cancer survival based on clinical data using machine l" by Fatimah Abdulazim Altuhaifa, Khin Than Win et al.

Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. Whi

Google-scholar
Artificial-intelligence
Data-mining
Feature-selection
Lung-cancer
Machine-learning
Survival-prediction

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